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"Topic": models, code, and papers

Benchmarking in Optimization: Best Practice and Open Issues

Jul 07, 2020
Thomas Bartz-Beielstein, Carola Doerr, Jakob Bossek, Sowmya Chandrasekaran, Tome Eftimov, Andreas Fischbach, Pascal Kerschke, Manuel Lopez-Ibanez, Katherine M. Malan, Jason H. Moore, Boris Naujoks, Patryk Orzechowski, Vanessa Volz, Markus Wagner, Thomas Weise

This survey compiles ideas and recommendations from more than a dozen researchers with different backgrounds and from different institutes around the world. Promoting best practice in benchmarking is its main goal. The article discusses eight essential topics in benchmarking: clearly stated goals, well-specified problems, suitable algorithms, adequate performance measures, thoughtful analysis, effective and efficient designs, comprehensible presentations, and guaranteed reproducibility. The final goal is to provide well-accepted guidelines (rules) that might be useful for authors and reviewers. As benchmarking in optimization is an active and evolving field of research this manuscript is meant to co-evolve over time by means of periodic updates.

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Real or Not Real, that is the Question

Feb 12, 2020
Yuanbo Xiangli, Yubin Deng, Bo Dai, Chen Change Loy, Dahua Lin

While generative adversarial networks (GAN) have been widely adopted in various topics, in this paper we generalize the standard GAN to a new perspective by treating realness as a random variable that can be estimated from multiple angles. In this generalized framework, referred to as RealnessGAN, the discriminator outputs a distribution as the measure of realness. While RealnessGAN shares similar theoretical guarantees with the standard GAN, it provides more insights on adversarial learning. Compared to multiple baselines, RealnessGAN provides stronger guidance for the generator, achieving improvements on both synthetic and real-world datasets. Moreover, it enables the basic DCGAN architecture to generate realistic images at 1024*1024 resolution when trained from scratch.

* ICLR2020 spotlight. 1) train GAN by maximizing kl-divergence. 2) train non-progressive GAN (DCGAN) architecture at 1024*1024 resolution 

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Deep Learning for Learning Graph Representations

Jan 02, 2020
Wenwu Zhu, Xin Wang, Peng Cui

Mining graph data has become a popular research topic in computer science and has been widely studied in both academia and industry given the increasing amount of network data in the recent years. However, the huge amount of network data has posed great challenges for efficient analysis. This motivates the advent of graph representation which maps the graph into a low-dimension vector space, keeping original graph structure and supporting graph inference. The investigation on efficient representation of a graph has profound theoretical significance and important realistic meaning, we therefore introduce some basic ideas in graph representation/network embedding as well as some representative models in this chapter.

* 51 pages, 8 figures 

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Higher Criticism for Discriminating Word-Frequency Tables and Testing Authorship

Oct 30, 2019
Alon Kipnis

We adapt the Higher Criticism (HC) goodness-of-fit test to detect changes between word frequency tables. We apply the test to authorship attribution, where the goal is to identify the author of a document using other documents whose authorship is known. The method is simple yet performs well without handcrafting and tuning. As an inherent side effect, the HC calculation identifies a subset of discriminating words. In practice, the identified words have low variance across documents belonging to a corpus of homogeneous authorship. We conclude that in testing a new document against the corpus of an author, HC is mostly affected by words characteristic of that author and is relatively unaffected by topic structure.

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From web crawled text to project descriptions: automatic summarizing of social innovation projects

May 22, 2019
Nikola Milosevic, Dimitar Marinov, Abdullah Gok, Goran Nenadic

In the past decade, social innovation projects have gained the attention of policy makers, as they address important social issues in an innovative manner. A database of social innovation is an important source of information that can expand collaboration between social innovators, drive policy and serve as an important resource for research. Such a database needs to have projects described and summarized. In this paper, we propose and compare several methods (e.g. SVM-based, recurrent neural network based, ensambled) for describing projects based on the text that is available on project websites. We also address and propose a new metric for automated evaluation of summaries based on topic modelling.

* Preceeding of 24th International Conference on Applications of Natural Language to Information Systems (NLDB2019) 
* Keywords: Summarization, evaluation metrics, text mining, natural language processing, social innovation, SVM, neural networks Accepted for publication in Proceedings of 24th International Conference on Applications of Natural Language to Information Systems (NLDB2019) 

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Mappa Mundi: An Interactive Artistic Mind Map Generator with Artificial Imagination

May 09, 2019
Ruixue Liu, Baoyang Chen, Meng Chen, Youzheng Wu, Zhijie Qiu, Xiaodong He

We present a novel real-time, collaborative, and interactive AI painting system, Mappa Mundi, for artistic Mind Map creation. The system consists of a voice-based input interface, an automatic topic expansion module, and an image projection module. The key innovation is to inject Artificial Imagination into painting creation by considering lexical and phonological similarities of language, learning and inheriting artist's original painting style, and applying the principles of Dadaism and impossibility of improvisation. Our system indicates that AI and artist can collaborate seamlessly to create imaginative artistic painting and Mappa Mundi has been applied in art exhibition in UCCA, Beijing

* Paper submmited for IJCAI 2019 Demo track 

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Automated Verification of Neural Networks: Advances, Challenges and Perspectives

May 25, 2018
Francesco Leofante, Nina Narodytska, Luca Pulina, Armando Tacchella

Neural networks are one of the most investigated and widely used techniques in Machine Learning. In spite of their success, they still find limited application in safety- and security-related contexts, wherein assurance about networks' performances must be provided. In the recent past, automated reasoning techniques have been proposed by several researchers to close the gap between neural networks and applications requiring formal guarantees about their behavior. In this work, we propose a primer of such techniques and a comprehensive categorization of existing approaches for the automated verification of neural networks. A discussion about current limitations and directions for future investigation is provided to foster research on this topic at the crossroads of Machine Learning and Automated Reasoning.

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Relating Eye-Tracking Measures With Changes In Knowledge on Search Tasks

May 07, 2018
Nilavra Bhattacharya, Jacek Gwizdka

We conducted an eye-tracking study where 30 participants performed searches on the web. We measured their topical knowledge before and after each task. Their eye-fixations were labelled as "reading" or "scanning". The series of reading fixations in a line, called "reading-sequences" were characterized by their length in pixels, fixation duration, and the number of fixations making up the sequence. We hypothesize that differences in knowledge-change of participants are reflected in their eye-tracking measures related to reading. Our results show that the participants with higher change in knowledge differ significantly in terms of their total reading-sequence-length, reading-sequence-duration, and number of reading fixations, when compared to participants with lower knowledge-change.

* Proceedings of the 2018 ACM Symposium on Eye Tracking Research & Applications 
* ACM Symposium on Eye Tracking Research and Applications (ETRA), June 14-17, 2018, Warsaw, Poland 

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Story Generation and Aviation Incident Representation

Feb 13, 2018
Peter Clark

This working note discusses the topic of story generation, with a view to identifying the knowledge required to understand aviation incident narratives (which have structural similarities to stories), following the premise that to understand aviation incidents, one should at least be able to generate examples of them. We give a brief overview of aviation incidents and their relation to stories, and then describe two of our earlier attempts (using `scripts' and `story grammars') at incident generation which did not evolve promisingly. Following this, we describe a simple incident generator which did work (at a `toy' level), using a `world simulation' approach. This generator is based on Meehan's TALE-SPIN story generator (1977). We conclude with a critique of the approach.

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Chinese Poetry Generation with Planning based Neural Network

Dec 07, 2016
Zhe Wang, Wei He, Hua Wu, Haiyang Wu, Wei Li, Haifeng Wang, Enhong Chen

Chinese poetry generation is a very challenging task in natural language processing. In this paper, we propose a novel two-stage poetry generating method which first plans the sub-topics of the poem according to the user's writing intent, and then generates each line of the poem sequentially, using a modified recurrent neural network encoder-decoder framework. The proposed planning-based method can ensure that the generated poem is coherent and semantically consistent with the user's intent. A comprehensive evaluation with human judgments demonstrates that our proposed approach outperforms the state-of-the-art poetry generating methods and the poem quality is somehow comparable to human poets.

* Accepted paper at COLING 2016 

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